app.py

from fastrtc import (
    ReplyOnPause, AdditionalOutputs, Stream,
    audio_to_bytes, aggregate_bytes_to_16bit
)
import gradio as gr
import numpy as np
import torch
import os
import tempfile
from transformers import (
    AutoModelForSpeechSeq2Seq, 
    AutoProcessor, 
    pipeline,
    AutoTokenizer, 
    AutoModelForCausalLM
)
from gtts import gTTS
from scipy.io import wavfile

# Check if CUDA is available, otherwise use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Step 1: Audio transcription with Whisper
def load_asr_model():
    model_id = "openai/whisper-small"
    
    model = AutoModelForSpeechSeq2Seq.from_pretrained(
        model_id, 
        torch_dtype=torch_dtype,
        low_cpu_mem_usage=True,
        use_safetensors=True
    )
    model.to(device)
    
    processor = AutoProcessor.from_pretrained(model_id)
    
    return pipeline(
        "automatic-speech-recognition",
        model=model,
        tokenizer=processor.tokenizer,
        feature_extractor=processor.feature_extractor,
        max_new_tokens=128,
        chunk_length_s=30,
        batch_size=16,
        return_timestamps=False,
        torch_dtype=torch_dtype,
        device=device,
    )

# Step 2: Text generation with a smaller LLM
def load_llm_model():
    model_id = "facebook/opt-1.3b"
    
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        torch_dtype=torch_dtype,
        low_cpu_mem_usage=True
    )
    model.to(device)
    
    return model, tokenizer

# Step 3: Text-to-Speech with gTTS (Google Text-to-Speech)
def gtts_text_to_speech(text):
    # Create a temporary file
    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as f:
        tmp_filename = f.name
    
    # Use gTTS to convert text to speech
    tts = gTTS(text=text, lang='en', slow=False)
    
    # Save as MP3 first (gTTS only outputs MP3)
    mp3_filename = tmp_filename.replace('.wav', '.mp3')
    tts.save(mp3_filename)
    
    # Convert MP3 to WAV using FFmpeg if available, otherwise use a fallback
    try:
        import subprocess
        subprocess.run(['ffmpeg', '-i', mp3_filename, '-acodec', 'pcm_s16le', '-ar', '24000', '-ac', '1', tmp_filename], 
                       stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    except (ImportError, FileNotFoundError):
        # Fallback if FFmpeg is not available
        from pydub import AudioSegment
        sound = AudioSegment.from_mp3(mp3_filename)
        sound = sound.set_frame_rate(24000).set_channels(1)
        sound.export(tmp_filename, format="wav")
    
    # Read the WAV file
    sample_rate, audio_data = wavfile.read(tmp_filename)
    
    # Clean up temporary files
    os.remove(mp3_filename)
    os.remove(tmp_filename)
    
    # Convert to expected format
    audio_data = audio_data.reshape(1, -1).astype(np.int16)
    
    return (sample_rate, audio_data)

# Initialize models
print("Loading ASR model...")
asr_pipeline = load_asr_model()

print("Loading LLM model...")
llm_model, llm_tokenizer = load_llm_model()

# Chat history management
chat_history = []

def generate_response(prompt):
    # If chat history is empty, add a system message
    if not chat_history:
        chat_history.append({"role": "system", "content": "You are a helpful, friendly AI assistant. Keep your responses concise and conversational."})
    
    # Add user message to history
    chat_history.append({"role": "user", "content": prompt})
    
    # Prepare input for the model
    full_prompt = ""
    for message in chat_history:
        if message["role"] == "system":
            full_prompt += f"System: {message['content']}\n"
        elif message["role"] == "user":
            full_prompt += f"User: {message['content']}\n"
        elif message["role"] == "assistant":
            full_prompt += f"Assistant: {message['content']}\n"
    
    full_prompt += "Assistant: "
    
    # Generate response with explicit attention mask
    inputs = llm_tokenizer(
        full_prompt, 
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=512,
        return_attention_mask=True  # Explicitly request attention mask
    ).to(device)
    
    with torch.no_grad():
        output = llm_model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],  # Pass the attention mask
            max_new_tokens=128,
            do_sample=True,
            temperature=0.7,
            top_p=0.9
        )
    
    response_text = llm_tokenizer.decode(output[0], skip_special_tokens=True)
    response_text = response_text.split("Assistant: ")[-1].strip()
    
    # Add assistant response to history
    chat_history.append({"role": "assistant", "content": response_text})
    
    # Keep history at a reasonable size
    if len(chat_history) > 10:
        # Keep system message and last 9 exchanges
        chat_history.pop(1)
    
    return response_text

def response(audio: tuple[int, np.ndarray]):
    # Step 1: Convert audio to float32 before passing to ASR
    sample_rate, audio_data = audio
    
    # Convert int16 audio to float32
    audio_float32 = audio_data.flatten().astype(np.float32) / 32768.0  # Normalize to [-1.0, 1.0]
    
    # Speech-to-Text with correct data type
    transcript = asr_pipeline({
        "sampling_rate": sample_rate, 
        "raw": audio_float32
    })
    
    prompt = transcript["text"]
    print(f"Transcribed: {prompt}")
    
    # Step 2: Generate text response
    response_text = generate_response(prompt)
    print(f"Response: {response_text}")
    
    # Step 3: Text-to-Speech using gTTS
    sample_rate, audio_array = gtts_text_to_speech(response_text)
    
    # Convert to expected format and yield chunks
    chunk_size = int(sample_rate * 0.2)  # 200ms chunks
    for i in range(0, audio_array.shape[1], chunk_size):
        chunk = audio_array[:, i:i+chunk_size]
        if chunk.size > 0:  # Ensure we don't yield empty chunks
            yield (sample_rate, chunk)

stream = Stream(
    modality="audio",
    mode="send-receive",
    handler=ReplyOnPause(response),
)

# For testing without WebRTC
def demo():
    with gr.Blocks() as demo:
        gr.Markdown("# Local Voice Chatbot")
        audio_input = gr.Audio(sources=["microphone"], type="numpy")
        audio_output = gr.Audio()
        
        def process_audio(audio):
            if audio is None:
                return None
            
            sample_rate, audio_array = audio
            
            # Convert to float32 for ASR
            audio_float32 = audio_array.flatten().astype(np.float32) / 32768.0
            
            transcript = asr_pipeline({
                "sampling_rate": sample_rate, 
                "raw": audio_float32
            })
            
            prompt = transcript["text"]
            print(f"Transcribed: {prompt}")
            
            response_text = generate_response(prompt)
            print(f"Response: {response_text}")
            
            sample_rate, audio_array = gtts_text_to_speech(response_text)
            return (sample_rate, audio_array[0])
        
        audio_input.change(process_audio, inputs=[audio_input], outputs=[audio_output])
    
    demo.launch()

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--demo", action="store_true", help="Run Gradio demo instead of WebRTC")
    args = parser.parse_args()
    # hugging face issues
    demo()
    
    # if args.demo:
    #     demo()
    # else:
    #     # For running with FastRTC
    #     # You would need to add your FastRTC server code here
    #     pass